Image super-resolution reconstruction based on adaptive sparse representation

摘要

There are two problems in global over-complete dictionary: lack of adaptability to image localstructure and low computational efficiency. Based on the study of the adaptive sparse representationreconstruction, this paper obtained a series of corresponding sub-dictionaries by imageblock subset, then the optimal sub-dictionary for each reconstruction image block is adaptivelyselected, which can be more accurately sparse represented modeled to improve the effect andefficiency of the algorithm. In order to promote the ability of sparse representation model, nonlocalself-similarity prior item is introduced. Meanwhile, the nonlocal self-similarity model isimproved by using the idea of the bilateral filter, and the space distance restraint between pixelsis introduced to better keep the image edge information. Moreover, the nonlocal self-similar distancemeasure is improved to reduce the amount of calculation. Experimental results show thatthe proposed algorithm can effectively suppress noise effects and can maintain the image edgedetails, at thesametime, there are certain advantages atboth the peak signal tonoise ratio (PSNR)and visual effects.